Qwen 3.5 0.8B
By Alibaba · China
Updated 2026-07-13
Overview
Alibaba's ultra-compact 0.8B chat model with a 256k context window and a sub-1GB Q4 footprint, Apache 2.0 on Ollama. Runs on CPUs, integrated GPUs, and Raspberry Pi.
When to pick this model
- Embedded assistants on phones, SBCs, and microcontrollers with NPUs
- Cheap classification, routing, or instruction-following at scale
- Offline chat where memory and power budgets are tight
- Long-context retrieval scenarios that don't need deep reasoning
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 0.5 GB |
| Q5_K_M | 0.6 GB |
| Q8_0 | 0.9 GB |
| FP16 (no quantization) | 1.6 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Qwen 3.5 0.8B fits an 8 GB consumer card at Q4_K_M (0.5 GB). Stepping up to Q8_0 nearly doubles the footprint to 0.9 GB, and unquantized FP16 weights take 1.6 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Qwen 3.5 0.8B needs roughly 2 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 110 tokens/sec on entry-level GPUs, on the order of 170 tokens/sec on a mid-range card, and up to 220 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Qwen 3.5 0.8B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Qwen 3.5 0.8B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (1.6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (1.6 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (1.6 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (1.6 GB used) |
| 32 GB | RTX 5090 | FP16 (1.6 GB used) |
Which GPU should you buy to run Qwen 3.5 0.8B?
To run Qwen 3.5 0.8B locally at Q4, you need ~0.5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Negligible memory footprint — under 1GB at Q4
- 256k context, rare at this size
- Apache 2.0 distribution via Ollama
- Runs comfortably on CPU, integrated GPU, or Raspberry Pi
Limitations
- Reasoning quality is inherently limited at 0.8B
- Text-only — no vision capability
- Hugging Face distribution uses the Qwen license rather than Apache
Typical workloads
In our catalog grid, Qwen 3.5 0.8B is filed under Edge / On-device, Lightweight Laptop Assistant, Ultra-Compact RAG — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
The 250k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense Transformer · 0.8B parameters
Training: Qwen 3.5 family (Alibaba). Ultra-compact variant aligned for chat/instruct.
The right pick when you need a real LLM in under a gigabyte and don't need it to think hard.
Quick start
ollama run qwen3.5Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Qwen 3.5 0.8B need?
At the recommended Q4_K_M quantization, Qwen 3.5 0.8B needs about 0.5 GB of VRAM. Q8_0 takes 0.9 GB, and unquantized FP16 weights take 1.6 GB.
Can Qwen 3.5 0.8B run without a GPU?
Yes — with roughly 2 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Qwen 3.5 0.8B support?
Qwen 3.5 0.8B supports a 250k-token context window (256,000 tokens).
Can I use Qwen 3.5 0.8B commercially?
Yes. Qwen 3.5 0.8B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Qwen 3.5 0.8B on consumer hardware?
Our compatibility engine estimates on the order of 170 tokens/sec on a mid-range GPU and up to 220 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Qwen 3.5 0.8B should I download first?
Start with Q4_K_M (0.5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at FP16.